City-Scale Location Recognition
Top Cited Papers
- 1 June 2007
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 10636919,p. 1-7
- https://doi.org/10.1109/cvpr.2007.383150
Abstract
We look at the problem of location recognition in a large image dataset using a vocabulary tree. This entails finding the location of a query image in a large dataset containing 3times104 streetside images of a city. We investigate how the traditional invariant feature matching approach falls down as the size of the database grows. In particular we show that by carefully selecting the vocabulary using the most informative features, retrieval performance is significantly improved, allowing us to increase the number of database images by a factor of 10. We also introduce a generalization of the traditional vocabulary tree search algorithm which improves performance by effectively increasing the branching factor of a fixed vocabulary tree.Keywords
This publication has 10 references indexed in Scilit:
- Scalable Recognition with a Vocabulary TreePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Image Based Localization in Urban EnvironmentsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- An Image-Based System for Urban NavigationPublished by British Machine Vision Association and Society for Pattern Recognition ,2004
- HPAT Indexing for Fast Object/Scene Recognition Based on Local AppearancePublished by Springer Nature ,2003
- Video Google: a text retrieval approach to object matching in videosPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Object recognition with informative features and linear classificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Shape indexing using approximate nearest-neighbour search in high-dimensional spacesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Object recognition from local scale-invariant featuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1999
- Clustering to minimize the maximum intercluster distanceTheoretical Computer Science, 1985
- A Branch and Bound Algorithm for Computing k-Nearest NeighborsIEEE Transactions on Computers, 1975